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All Journal ComEngApp : Computer Engineering and Applications Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) TELKOMNIKA (Telecommunication Computing Electronics and Control) CommIT (Communication & Information Technology) Sisforma: Journal of Information Systems Journal of Information Systems Engineering and Business Intelligence EMITTER International Journal of Engineering Technology IJoICT (International Journal on Information and Communication Technology) E-Dimas: Jurnal Pengabdian kepada Masyarakat Fountain of Informatics Journal Journal of Information Technology and Computer Science Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING JMM (Jurnal Masyarakat Mandiri) JCES (Journal of Character Education Society) JUTEI (Jurnal Terapan Teknologi Informasi) International Journal of New Media Technology ABDIMAS SILIWANGI Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika Journal of Information Technology and Computer Engineering ComTech: Computer, Mathematics and Engineering Applications Altruis: Journal of Community Services Jurnal Abdimas Ilmiah Citra Bakti (JAICB) Journal of Technology and Informatics (JoTI) Abdimas Altruis: Jurnal Pengabdian Kepada Masyarakat Konstelasi: Konvergensi Teknologi dan Sistem Informasi Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Inovatif Wira Wacana JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Oscar Karnalim, Oscar
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Prediksi Analisis Sentimen Data Debat Pemilihan Presiden 2024 Menggunakan Support Vector Machine (SVM): Prediction of Sentiment Analysis of 2024 Presidential Election Debate Data Using Support Vector Machine (SVM) Kusman, Vardina Nava Madya; Metayani, Vanessa; Karnalim, Oscar
EXPLORE IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika Vol 16 No 1 (2024): Jurnal Explore IT Edisi June 2024
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Yudharta Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35891/explorit.v16i1.4887

Abstract

Penelitian ini bertujuan untuk mengembangkan model klasifikasi menggunakan Support Vector Machine untuk menganalisis sentimen pada data dialog debat Pemilihan Presiden tahun 2024. Sentimen dari ucapan tidak selalu dapat diketahui, sehingga model dalam penelitian ini diusulkan untuk menemukan sentimen dibalik ucapan. Untuk dapat memprediksi sentimen, model dilatih dengan data debat pilpres yang telah dikumpulkan. Model kemudian melakukan klasifikasi terhadap data tersebut, dan kemudian diuji tingkat akurasinya. Setelah diuji menggunakan data tes, diperoleh nilai akurasi sebesar 52,5%. Hasil tersebut kurang memuaskan, maka dilakukan optimasi terhadap model dan data, Hasilnya, nilai akurasi meningkat menjadi sekitar 94%. Untuk kedepannya, mungkin data yang digunakan bisa semakin ditingkatkan dengan memperhatikan distribusi kelas dalam data.
Pelatihan Guru untuk Tantangan Bebras 2022 di Biro Bebras Universitas Kristen Maranatha Ayub, Mewati; Karnalim, Oscar; Tan, Robby; Wijanto, Maresha Caroline; Edi, Doro; Bunyamin, Hendra; Kasih, Julianti; Yulianti, Diana Trivena; Widjaja, Andreas; Risal, Risal; Nathasya, Rossevine Artha
E-Dimas: Jurnal Pengabdian kepada Masyarakat Vol 14, No 3 (2023): E-DIMAS
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/e-dimas.v14i3.14326

Abstract

Tantangan Bebras merupakan salah satu kegiatan yang memperkenalkan computational thinking dan informatika kepada siswa sekolah. Bebras Indonesia melalui setiap mitra biro Bebras di seluruh Indonesia menyelenggarakan Tantangan Bebras setiap tahunnya yaitu pada minggu kedua bulan November. Biro Bebras Maranatha juga mempersiapkan guru-guru yang berada di bawah naungan Biro Bebras Maranatha dalam kegiatan pelatihan pada 7 Oktober 2022 secara hybrid dan technical meeting pada 28 Oktober 2022. Pelatihan untuk tahun 2022 dimulai dengan kuis soal-soal Bebras yang diambil dari soal-soal dalam Tantangan Bebras tahun-tahun sebelumnya untuk mengukur tingkat pemahaman guru dalam computational thinking. Kegiatan pelatihan dilanjutkan dengan pembahasan soal kuis melalui diskusi, penyampaian konsep computational thinking, serta pendaftaran dan persiapan siswa untuk Tantangan Bebras 2022. Pada akhir sesi pelatihan, guru-guru peserta mengisi kuesioner untuk mengetahui sejauh mana persiapan yang sudah dilakukan untuk Tantangan Bebras 2022. Pelaksanaan kegiatan dilaksanakan secara hybrid diikuti oleh 52 guru perwakilan sekolah. Dari 52 guru yang mengikuti kuis, nilai kuis berkisar antara 0 sampai 80 di mana rata-rata nilai adalah 35. Sebanyak 79% dari guru-guru yang mengikuti pelatihan ini sudah pernah mengikuti workshop Bebras di tahun-tahun sebelumnya dan 69% dari total guru tersebut telah memanfaatkan soal Bebras untuk pembelajaran di kelas. Selama proses pembekalan Tantangan Bebras, terdapat tiga tantangan terbesar yang dihadapi yaitu kemampuan berpikir siswa, persiapan guru untuk pembekalan, dan melatih siswa dalam membaca soal.
Pengembangan Computational Thinking Siswa melalui Tantangan Bebras 2023 di Biro Bebras Universitas Kristen Maranatha Ayub, Mewati; Tan, Robby; Wijanto, Maresha Caroline; Nathasya, Rossevine Artha; Adelia, Adelia; Senjaya, Wenny Franciska; Karnalim, Oscar; Surjawan, Daniel Jahja; Edi, Doro; Toba, Hapnes; Christianti, Meliana; Kasih, Julianti; Risal, Risal; Yulianti, Diana Trivena; Zakaria, Teddy Marcus; Liliawati, Swat Lie
E-Dimas: Jurnal Pengabdian kepada Masyarakat Vol 15, No 3 (2024): E-DIMAS
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/e-dimas.v15i3.18162

Abstract

Pengabdian masyarakat yang dilakukan bertujuan untuk mengembangkan kemampuan Computational Thinking (CT) siswa melalui kegiatan Tantangan Bebras. Tantangan Bebras adalah kegiatan untuk memberi tantangan kepada siswa berupa sekumpulan Bebras task yang harus diselesaikan dalam waktu terbatas. Bebras task mengandung konsep Computational Thinking dan informatika yang dikemas dalam bentuk persoalan yang harus dipecahkan. Tantangan Bebras diadakan oleh Bebras Indonesia setiap tahun pada minggu kedua bulan November dengan melibatkan mitra Biro Bebras di seluruh Indonesia. Biro Bebras Universitas Kristen Maranatha mempersiapkan guru pendamping siswa melalui pelatihan guru agar dapat membimbing siswa dalam berlatih memecahkan Bebras task. Dalam pelatihan, guru diperkenalkan dengan Bebras task melalui kuis yang kemudian dibahas bersama. Guru juga diberi materi pengenalan CT dan aktivitas unplugged. Masa pendaftaran peserta Tantangan Bebras dilakukan setelah pelatihan, pendaftaran dilakukan secara kolektif melalui sekolah. Ada 4 kategori lomba, yaitu SiKecil untuk SD kelas 1-3, Siaga untuk SD kelas 4-6, Penggalang untuk SMP, dan Penegak untuk SMA. Terdapat 54 sekolah yang mendaftarkan siswanya. Menjelang hari Tantangan diadakan technical meeting untuk guru sebagai persiapan untuk mendampingi siswa pada saat uji coba akun dan pada saat tantangan. Peserta yang mengikuti Tantangan melalui Biro Bebras UK Maranatha berjumlah 3429 orang, yang terbanyak adalah kategori Penggalang. Hasil Tantangan menunjukkan kategori Siaga dan SiKecil sudah baik, sedangkan kategori Penggalang dan Penegak perlu mempersiapkan diri lebih baik di tahun mendatang.
Topic Analysis Video Debat Jelang Pemilu Presiden dan Wakil Presiden Tahun 2024 Valentina, Ivana; Mu’min, Aziz; Tanrico, Devion; Karnalim, Oscar
Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Vol 12 No 1 (2024): TEKNOIF APRIL 2024
Publisher : ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21063/jtif.2024.V12.1.29-35

Abstract

Several debates were held among presidential and vice presidential candidates to convey their ideas for the 2024 presidential general election (PEMILU). This research analyzes the topics discussed in the debates using Latent Dirichlet Allocation (LDA), K-means Clustering, and word tagging methods for each candidate pair. The K-Means Clustering method yielded more diverse and evenly distributed topics for each candidate pair, while LDA produced fewer topics but was more effective in identifying topics for candidate A. The K- Means Clustering method yielded more diverse and evenly distributed topics for each candidate pair, while LDA produced fewer topics but was more effective in identifying topics for candidate. These are somewhat consistent with previos works. A. In dataset 1 using the LDA model, candidate pairs A have a probability of 60%, B have a probability of 25%, and C have a probability of 0%. In dataset 2 using the K-Means model, candidate pairs A have a probability of 37.04%, B have a probability of 25%, and C have a probability of 17.24%. In dataset 2 using the LDA model, candidate pairs A have a probability of 100%, B have a probability of 40%, and C have a probability of 0%. In dataset 2 using the K-Means model, candidate pairs A have a probability of 35.71%, B have a probability of 14.29%, and C have a probability of 28.57%.
Analisis Klaster Kriteria Gangguan Kecemasan Sosial Berdasarkan Fase Perawatannya Wiwaha, Panji Yudasetya; Toba, Hapnes; Karnalim, Oscar
Jurnal Teknik Informatika dan Sistem Informasi Vol 10 No 1 (2024): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v10i1.8400

Abstract

This study aims to cluster the activity dataset of patients who suffer from social anxiety disorder at a Mental Healthcare Company located in the Netherlands and measure the affinity of the cluster to the identified treatment phase based on the similarity of its feature density. The methodology of data clustering is carried out in the following way: 1) data pre-processing against the anonymous patient data, communication data, tracker data of the social anxiety disorder, registration history of the daily entry, notification data, planned event completion data, questionnaires related to the relevancy of the treatment, history of the patient's treatments, and registration history of the thought record, 2) exploratory data analysis to visualize the data point distribution of the activity dataset, perform data standardization, and find the optimal number of clusters, and 3) building a clustering model using the k-Means algorithm. The effectiveness of data clustering is validated by 1) comparing the affinity of clusters to the identified treatment phase and 2) calculating the feature weights to find any features with unique characteristics (dominant) in each treatment phase. The k-Means model successfully grouped the activity dataset into 10 clusters. The clusters are analyzed based on the pattern of cluster affinity and its percentage ratio. Then, 3 clusters are selected because they are close enough to represent each treatment phase in the Mental Healthcare Company. The findings in this study show that the number of days since the patient made a registration, the number of registrations related to social anxiety disorder in the past week, the comparison of negative registrations in the past week compared to one week before, questionnaire scores related to treatment relevancies, and low scores in any questionnaire indicators are distinguished features for each treatment phase. In addition, the urgency of those features matches the therapist's top priority list when treating their clients. Nonetheless, further and comprehensive research must be conducted to understand the impact of the dominant features in each cluster so the classification model for creating a list of recommended patients based on their urgency level of treatment can be built.
Analisis Perbandingan Algoritma Machine Learning untuk Forecasting Persediaan Produk Barang Pokok Avinash, Avinash; Widjaja, Andreas; Karnalim, Oscar
Jurnal Teknik Informatika dan Sistem Informasi Vol 10 No 2 (2024): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v10i2.9357

Abstract

Abstract —In the era of continuously evolving technology, consumers' demands for everyday needs are becoming more complex.Retail companies must adopt sophisticated approaches to understand and meet consumer preferences. This research explores theeffectiveness of Machine Learning algorithms in forecasting inventory levels in various types of retail stores using historicaltransaction payment data and related variables. One approach used is data transformation using one standard deviation intervalto stationarize data, ensuring statistical consistency that is important for prediction algorithms. The research results show that theSeasonal Autoregressive Integrated Moving Average (SARIMA) algorithm performs best in predicting inventory levels for bothSMEs and modern retailers. For the original data, the Mean Absolute Percentage Error (MAPE) for SMEs is 1.11% and formodern retailers is 0.98%. For data modified with one standard deviation interval, the MAPE for SMEs is 0.74% and for modernretailers is 0.70%. These results indicate superior prediction accuracy, helping companies adjust their inventory levels moreaccurately according to market dynamics and consumer expectations. This research is expected to provide a solid guideline forimproving inventory management strategies, enabling companies to prepare inventory levels more accurately according to marketdynamics and consumer expectations.Keywords—Forecasting Optimization, Machine Learning Algorithm Comparison, Inventory Levels, Modern Retail, SMEs, Onestandard deviation interval
An Embedding Technique for Language-Independent Lecturer-Oriented Program Visualization Sulistiani, Lisan; Karnalim, Oscar
EMITTER International Journal of Engineering Technology Vol 6 No 1 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (512.238 KB) | DOI: 10.24003/emitter.v6i1.234

Abstract

Nowadays, programming is a promising skill to be learned; the demand of programmer is increased. To align with such trend, several Program Visualization (PV) tools have been developed. Using such tool, user can learn how a particular program works through interactive and descriptive visualization. However, most of the tools are language-dependent: they use either language-dependent debugger or code to generate visualization. Such dependency may become a problem when a program written in new programming language is incorporated. Therefore, this paper proposes an embedding technique to handle given issue. To incorporate new programming language, it only needs five language-dependent features to be set. In general, our proposed technique works in threefold: embedding some statements to target program, generating visualization states by running the program with console commands, and visualizing given program based on generated visualization states. According to our evaluation, proposed technique is able to incorporate program written in any programming languages as long as those languages provide required language-dependent features. Further, it is practical to be used since it still have the benefits of conventional PV even though it is designed as a language-independent PV.
Co-Authors ADELIA Adelia Adelia, Adelia Aditya Permadi Aditya Permadi Aldi Aldiansyah Andreas Widjaja Andreas Widjaja Andrisyah Andrisyah Andrisyah Andrisyah Annabel, Kathleen Felicia Avinash, Avinash Aziz Mu’min Bayu Rima Aditya Bertha Alan Manuel Bertha Alan Manuel Daniel Jahja Surjawan Devion Tanrico Diana Trivena Yulianti Dina Fitria Murad Dina Fitria Murad Doro Edi Egie Imandha, Egie Elvina Elvina Elvina Elvina Erico Darmawan Handoyo Fathul Jannah Felicia Annabel, Kathleen Felix Christian Jonathan Felix Christian Jonathan Felix Christian Jonathan Gisela Kurniawati Haba Ito, Ridolof Hapnes Toba Hendra Bunyamin Hendra Bunyamin Hendra Bunyamin Irawan Nurhas Iryanto Faot, Pace Ivana Valentina Johan, Meliana Christianti Julianti Kasih Julianti Kasih, Julianti Kurniawan, Phin Kurniawati, Gisela Kusman, Vardina Nava Madya Lemmuela , Ivana Valentina Liliawati, Swat Lie Lucky Christiawan Lucky Christiawan, Lucky Majiah, Arya Tri Putra Maresha Caroline Wijanto Maresha Caroline Wijanto Maresha Caroline Wijanto Maresha Caroline Wijanto Maresha Caroline Wijanto Maresha Caroline Wijanto Marlina Marlina Martua, Juan Sterling Metayani, Vanessa Mewati Ayub Mulyono, Yovie Adhisti Mu’min, Aziz Oscar Wongso Pangestu, Muftah Afrizal Panji Yudasetya Wiwaha Rachmi Rachmadiany Ricardo Franclinton Risal Risal Risal Robby Tan Rossevine Artha Nathasya Ruis, Nisa Deviani Agustin Samosir, Moses Marzuki Santiadi, Sherly Sendy Ferdian Sujadi Setia Budi Setia Budi Setiawan, Yehezkiel David Simalango, Veronica Marcella Angela Sofriesilero Zumaytis Sulaeman Santoso Sulistiani, Lisan Sulistiani, Lisan Tanrico, Devion Teddy Marcus Zakaria Teddy Marcus Zakaria Tendy Cahyadi, Tendy Tjatur Kandaga Valentina, Ivana Vanessa Metayani Vardina Nava Madya Kusman Vincent Elbert Budiman Wenny Franciska Senjaya Wijaya, Bernadus Indra Wiwaha, Panji Yudasetya Yan Sen Paulus Yudha, Laurentius Gusti Ontoseno Panata Zaqi Megantara, Rizky